The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis

Mohammad Amin Habibi; Fateme Aghaei; Zohreh Tajabadi; Mohammad Sina Mirjani; Poriya Minaee; SeyedMohammad Eazi

doi:10.1016/j.wneu.2023.11.061

The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis

World Neurosurg. 2023 Nov 22:S1878-8750(23)01624-8. doi: 10.1016/j.wneu.2023.11.061. Online ahead of print.

Authors

Mohammad Amin Habibi¹, Fateme Aghaei², Zohreh Tajabadi³, Mohammad Sina Mirjani², Poriya Minaee², SeyedMohammad Eazi²

Affiliations

¹ Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran. Electronic address: mohammad.habibi1392@yahoo.com.
² Student Research Committee, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran.
³ Digestive Disease Research Institute, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran.

PMID: 37995996
DOI: 10.1016/j.wneu.2023.11.061

Abstract

Background: Diffuse midline gliomas (DMGs) encompass a set of tumors, and those tumors with H3K27 M mutation carry a poor prognosis. In recent years, machine learning (ML)-based radiomics have shown promising results in predicting gene mutation status non-invasively. Therefore, this study aims to comprehensively evaluate the diagnostic performance of ML-based magnetic resonance imaging radiomics in predicting H3K27 M mutation status in DMG patients.

Methods: A systematic search was conducted using relevant keywords in PubMed/Medline, Scopus, Embase, and Web of Science from inception to May 2023. Original studies evaluating the diagnostic performance of ML models in predicting H3K27 M mutation status in DMGs were enrolled. Quality assessment of the enrolled studies was conducted using QUADAS-2. Data were analyzed using STATA version 17.0 to calculate pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic score, and diagnostic odds ratio.

Results: A total of 13 studies, including 12 retrospectives and 1 both retrospective and prospective study, enrolled 1510 (male = 777) DMG patients. Six studies underwent meta-analysis which showed a pooled sensitivity, specificity, positive and negative likelihood ratio, diagnostic score, and diagnostic odds ratio of 0.91 (95% CI 0.77-0.97), 0.81 (95% CI 0.73-0.88), 4.86 (95% CI 3.25-7.24), 0.11 (95% CI 0.04-0.29), 3.75 (95% CI 2.62-4.88), and 42.61 (95% CI 13.77-131.87), respectively.

Conclusions: Non-invasive prediction of H3K27 M mutation status in patients with DMGs using magnetic resonance imaging radiomics is a promising tool with good diagnostic performance. However, the pooled metrics had a wide confidence interval, which required further studies to enhance ML algorithms' accuracy and facilitate their integration into daily clinical practice.

Keywords: Glioma; Machine learning; Magnetic resonance imaging; Prognosis.